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Exponential Stability of Discrete-Time Cellular Uncertain BAM Neural Networks

In: Stability Analysis of Neural Networks

Author

Listed:
  • Grienggrai Rajchakit

    (Maejo University, Department of Mathematics)

  • Praveen Agarwal

    (Ajman University, Nonlinear Dynamics Research Center)

  • Sriraman Ramalingam

    (Kalasalingam Academy of Research and Education, Department of Mathematics)

Abstract

In this chapter, a class of uncertain discrete-time cellular BAMNN models with variable time delays is studied. The global stability in the exponential sense of the considered time-delayed cellular BAMNN model is analyzed by employing a discrete analog type of Halanay-type inequality Halanay-type inequality. We prove the stability conditions by using a time-invariant perturbation matrix, which is often known as parameter uncertainties. Illustrative examples are provided to ascertain the usefulness and flexibility of the proposed method.

Suggested Citation

  • Grienggrai Rajchakit & Praveen Agarwal & Sriraman Ramalingam, 2021. "Exponential Stability of Discrete-Time Cellular Uncertain BAM Neural Networks," Springer Books, in: Stability Analysis of Neural Networks, chapter 0, pages 253-274, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-6534-9_8
    DOI: 10.1007/978-981-16-6534-9_8
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